To　improve　the　performance　of　the　naive　Bayes　classifier,　a　method　is　proposed　which　regulates　text　categories　by　adding　adjustment　values　to　the　output　of　the　naive　Bayes　classifier.　The　classification　pattern　was　learned　in　an　incremental　and　adaptive　way,　and　the　interval　during　which　the　output　of　the　naive　Bayes　classifier　should　be　adjusted　was　built　according　to　the　classification　performance　evaluated　by　historical　outputs　Then　the　adjustment　value　was　adaptively　added　to　the　output　of　the　naive　Bayes　classifier　distributed　in　the　interval　to　regulate　its　category.　The　experiment　results　on　Trec05,　Trec06,　Trec07,　CEAS08　datasets　show　that　the　proposed　method　outperforms　the　naive　Bayes　classifier　and　the　bagging　naive　Bayes　classifier　in　terms　of　accuracy,　Macro　Fj　,　in　addition　to　its　simplicity　and　practicality.